Build an entire application with your app server and database with one seamless API.

Eliminate the need for data mapping back and forth from GraphQL to the database schema.  Get better performance, greater scalability, and simpler maintenance.

Easily build two-tower, collaborative filtering-based, or KNN recommendation systems.

Store computed similarity as additional knowledge in the graph for an efficient real-time recommendation engine.

Identify and match real-world entities in unstructured data sources. Dgraph’s ability to store, link, and traverse nodes enables data standardization, duplicate detection, and data merging.

Clean data is at the heart of any application.

Represent knowledge as a network of entities and their relationships. By materializing the relationships between entities, it creates critical context for AI.

When building with LLMs, a knowledge graph increases performance, response accuracy, and improves explainability.

Understand how your buyers go from intent to purchase.

By modeling the inputs of your customer journey-- retailers can create a personalized experience for each buyer.

Implement real-time product recommendations and promotions.

Detect complex fraud involving multiple entities and accounts with Dgraph.  

Unlike databases that rely on JOINs, Dgraph enables the analysis of large connected data sets to identify crucial relationships and patterns, resulting in more accurate and effective fraud detection.